Evolutionary Data Clustering Algorithms And Applications

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Evolutionary Data Clustering: Algorithms and Applications

Author : Ibrahim Aljarah
Publisher : Springer Nature
ISBN 13 : 9813341912
Total Pages : 248 pages
Book Rating : 4.8/5 (133 download)

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Book Synopsis Evolutionary Data Clustering: Algorithms and Applications by : Ibrahim Aljarah

Download or read book Evolutionary Data Clustering: Algorithms and Applications written by Ibrahim Aljarah and published by Springer Nature. This book was released on 2021-02-20 with total page 248 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an in-depth analysis of the current evolutionary clustering techniques. It discusses the most highly regarded methods for data clustering. The book provides literature reviews about single objective and multi-objective evolutionary clustering algorithms. In addition, the book provides a comprehensive review of the fitness functions and evaluation measures that are used in most of evolutionary clustering algorithms. Furthermore, it provides a conceptual analysis including definition, validation and quality measures, applications, and implementations for data clustering using classical and modern nature-inspired techniques. It features a range of proven and recent nature-inspired algorithms used to data clustering, including particle swarm optimization, ant colony optimization, grey wolf optimizer, salp swarm algorithm, multi-verse optimizer, Harris hawks optimization, beta-hill climbing optimization. The book also covers applications of evolutionary data clustering in diverse fields such as image segmentation, medical applications, and pavement infrastructure asset management.

Data Clustering: Theory, Algorithms, and Applications, Second Edition

Author : Guojun Gan
Publisher : SIAM
ISBN 13 : 1611976332
Total Pages : 430 pages
Book Rating : 4.6/5 (119 download)

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Book Synopsis Data Clustering: Theory, Algorithms, and Applications, Second Edition by : Guojun Gan

Download or read book Data Clustering: Theory, Algorithms, and Applications, Second Edition written by Guojun Gan and published by SIAM. This book was released on 2020-11-10 with total page 430 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data clustering, also known as cluster analysis, is an unsupervised process that divides a set of objects into homogeneous groups. Since the publication of the first edition of this monograph in 2007, development in the area has exploded, especially in clustering algorithms for big data and open-source software for cluster analysis. This second edition reflects these new developments, covers the basics of data clustering, includes a list of popular clustering algorithms, and provides program code that helps users implement clustering algorithms. Data Clustering: Theory, Algorithms and Applications, Second Edition will be of interest to researchers, practitioners, and data scientists as well as undergraduate and graduate students.

Relational Data Clustering

Author : Bo Long
Publisher : CRC Press
ISBN 13 : 1420072625
Total Pages : 214 pages
Book Rating : 4.4/5 (2 download)

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Book Synopsis Relational Data Clustering by : Bo Long

Download or read book Relational Data Clustering written by Bo Long and published by CRC Press. This book was released on 2010-05-19 with total page 214 pages. Available in PDF, EPUB and Kindle. Book excerpt: A culmination of the authors' years of extensive research on this topic, Relational Data Clustering: Models, Algorithms, and Applications addresses the fundamentals and applications of relational data clustering. It describes theoretic models and algorithms and, through examples, shows how to apply these models and algorithms to solve real-world problems. After defining the field, the book introduces different types of model formulations for relational data clustering, presents various algorithms for the corresponding models, and demonstrates applications of the models and algorithms through extensive experimental results. The authors cover six topics of relational data clustering: Clustering on bi-type heterogeneous relational data Multi-type heterogeneous relational data Homogeneous relational data clustering Clustering on the most general case of relational data Individual relational clustering framework Recent research on evolutionary clustering This book focuses on both practical algorithm derivation and theoretical framework construction for relational data clustering. It provides a complete, self-contained introduction to advances in the field.

Data Science Concepts and Techniques with Applications

Author : Usman Qamar
Publisher : Springer Nature
ISBN 13 : 3031174429
Total Pages : 492 pages
Book Rating : 4.0/5 (311 download)

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Book Synopsis Data Science Concepts and Techniques with Applications by : Usman Qamar

Download or read book Data Science Concepts and Techniques with Applications written by Usman Qamar and published by Springer Nature. This book was released on 2023-04-02 with total page 492 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook comprehensively covers both fundamental and advanced topics related to data science. Data science is an umbrella term that encompasses data analytics, data mining, machine learning, and several other related disciplines. The chapters of this book are organized into three parts: The first part (chapters 1 to 3) is a general introduction to data science. Starting from the basic concepts, the book will highlight the types of data, its use, its importance and issues that are normally faced in data analytics, followed by presentation of a wide range of applications and widely used techniques in data science. The second part, which has been updated and considerably extended compared to the first edition, is devoted to various techniques and tools applied in data science. Its chapters 4 to 10 detail data pre-processing, classification, clustering, text mining, deep learning, frequent pattern mining, and regression analysis. Eventually, the third part (chapters 11 and 12) present a brief introduction to Python and R, the two main data science programming languages, and shows in a completely new chapter practical data science in the WEKA (Waikato Environment for Knowledge Analysis), an open-source tool for performing different machine learning and data mining tasks. An appendix explaining the basic mathematical concepts of data science completes the book. This textbook is suitable for advanced undergraduate and graduate students as well as for industrial practitioners who carry out research in data science. They both will not only benefit from the comprehensive presentation of important topics, but also from the many application examples and the comprehensive list of further readings, which point to additional publications providing more in-depth research results or provide sources for a more detailed description of related topics. "This book delivers a systematic, carefully thoughtful material on Data Science." from the Foreword by Witold Pedrycz, U Alberta, Canada.

Computational Intelligence, Evolutionary Computing and Evolutionary Clustering Algorithms

Author : Terje Kristensen
Publisher : Bentham Science Publishers
ISBN 13 : 1681082993
Total Pages : 135 pages
Book Rating : 4.6/5 (81 download)

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Book Synopsis Computational Intelligence, Evolutionary Computing and Evolutionary Clustering Algorithms by : Terje Kristensen

Download or read book Computational Intelligence, Evolutionary Computing and Evolutionary Clustering Algorithms written by Terje Kristensen and published by Bentham Science Publishers. This book was released on 2016-09-30 with total page 135 pages. Available in PDF, EPUB and Kindle. Book excerpt: This brief text presents a general guideline for writing advanced algorithms for solving engineering and data visualization problems. The book starts with an introduction to the concept of evolutionary algorithms followed by details on clustering and evolutionary programming. Subsequent chapters present information on aspects of computer system design, implementation and data visualization. The book concludes with notes on the possible applications of evolutionary algorithms in the near future. This book is intended as a supplementary guide for students and technical apprentices learning machine language, or participating in advanced software programming, design and engineering courses.

Multiobjective Genetic Algorithms for Clustering

Author : Ujjwal Maulik
Publisher : Springer Science & Business Media
ISBN 13 : 3642166156
Total Pages : 292 pages
Book Rating : 4.6/5 (421 download)

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Book Synopsis Multiobjective Genetic Algorithms for Clustering by : Ujjwal Maulik

Download or read book Multiobjective Genetic Algorithms for Clustering written by Ujjwal Maulik and published by Springer Science & Business Media. This book was released on 2011-09-01 with total page 292 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the first book primarily dedicated to clustering using multiobjective genetic algorithms with extensive real-life applications in data mining and bioinformatics. The authors first offer detailed introductions to the relevant techniques – genetic algorithms, multiobjective optimization, soft computing, data mining and bioinformatics. They then demonstrate systematic applications of these techniques to real-world problems in the areas of data mining, bioinformatics and geoscience. The authors offer detailed theoretical and statistical notes, guides to future research, and chapter summaries. The book can be used as a textbook and as a reference book by graduate students and academic and industrial researchers in the areas of soft computing, data mining, bioinformatics and geoscience.

Data Clustering

Author : Guojun Gan
Publisher : SIAM
ISBN 13 : 9780898718348
Total Pages : 488 pages
Book Rating : 4.7/5 (183 download)

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Book Synopsis Data Clustering by : Guojun Gan

Download or read book Data Clustering written by Guojun Gan and published by SIAM. This book was released on 2007-01-01 with total page 488 pages. Available in PDF, EPUB and Kindle. Book excerpt: Cluster analysis is an unsupervised process that divides a set of objects into homogeneous groups. This book starts with basic information on cluster analysis, including the classification of data and the corresponding similarity measures, followed by the presentation of over 50 clustering algorithms in groups according to some specific baseline methodologies such as hierarchical, center-based, and search-based methods. As a result, readers and users can easily identify an appropriate algorithm for their applications and compare novel ideas with existing results. The book also provides examples of clustering applications to illustrate the advantages and shortcomings of different clustering architectures and algorithms. Application areas include pattern recognition, artificial intelligence, information technology, image processing, biology, psychology, and marketing. Readers also learn how to perform cluster analysis with the C/C++ and MATLAB programming languages.

Computational Intelligence for Big Data Analysis

Author : D.P. Acharjya
Publisher : Springer
ISBN 13 : 3319165984
Total Pages : 276 pages
Book Rating : 4.3/5 (191 download)

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Book Synopsis Computational Intelligence for Big Data Analysis by : D.P. Acharjya

Download or read book Computational Intelligence for Big Data Analysis written by D.P. Acharjya and published by Springer. This book was released on 2015-04-21 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt: The work presented in this book is a combination of theoretical advancements of big data analysis, cloud computing, and their potential applications in scientific computing. The theoretical advancements are supported with illustrative examples and its applications in handling real life problems. The applications are mostly undertaken from real life situations. The book discusses major issues pertaining to big data analysis using computational intelligence techniques and some issues of cloud computing. An elaborate bibliography is provided at the end of each chapter. The material in this book includes concepts, figures, graphs, and tables to guide researchers in the area of big data analysis and cloud computing.

Evolutionary Spectral Co-clustering

Author : Nathan S. Green
Publisher :
ISBN 13 :
Total Pages : 54 pages
Book Rating : 4.:/5 (656 download)

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Book Synopsis Evolutionary Spectral Co-clustering by : Nathan S. Green

Download or read book Evolutionary Spectral Co-clustering written by Nathan S. Green and published by . This book was released on 2010 with total page 54 pages. Available in PDF, EPUB and Kindle. Book excerpt: "The field of mining evolving data is relatively new and evolutionary clustering is among the latest in this trend. Presently, there are algorithms for evolutionary k-means, agglomerative hierarchical, and spectral clustering. These have been excellent in showing the advantages of using evolving data snapshots for better clustering results. From these algorithms the key portion of the conversion from static data handling to evolving data handling has been the addition of the historical cost function. The cost function is what determines whether or not instances should be moved from one cluster to the next between time-steps based on the historical cuts made between the instances in the dataset. These cost functions are then the method by which evolutionary clustering provides smooth transitions as there is a tunable tolerance for shifts in cluster membership. This also means that transitions between clusters become much more significant. For example, if an author-word matrix were clustered over ten years and an author changed clusters part way through the time-line it is a likely indicator that the author has changed research topics. Methods for mining evolving data have not yet expanded into co-clustering; for this reason I have contributed a new algorithm for co-clustering evolving data. The algorithm uses spectral co-clustering to cluster each time-step of instances and features. Using the previous example, cluster changes in features (or words) for an author-word matrix is significant in that it may indicate a change in meaning for the word. This contribution to the field provides an avenue for further development of evolutionary co-clustering algorithms."--Abstract.

Recent Advances in Hybrid Metaheuristics for Data Clustering

Author : Sourav De
Publisher : John Wiley & Sons
ISBN 13 : 1119551609
Total Pages : 196 pages
Book Rating : 4.1/5 (195 download)

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Book Synopsis Recent Advances in Hybrid Metaheuristics for Data Clustering by : Sourav De

Download or read book Recent Advances in Hybrid Metaheuristics for Data Clustering written by Sourav De and published by John Wiley & Sons. This book was released on 2020-06-02 with total page 196 pages. Available in PDF, EPUB and Kindle. Book excerpt: An authoritative guide to an in-depth analysis of various state-of-the-art data clustering approaches using a range of computational intelligence techniques Recent Advances in Hybrid Metaheuristics for Data Clustering offers a guide to the fundamentals of various metaheuristics and their application to data clustering. Metaheuristics are designed to tackle complex clustering problems where classical clustering algorithms have failed to be either effective or efficient. The authors noted experts on the topic provide a text that can aid in the design and development of hybrid metaheuristics to be applied to data clustering. The book includes performance analysis of the hybrid metaheuristics in relationship to their conventional counterparts. In addition to providing a review of data clustering, the authors include in-depth analysis of different optimization algorithms. The text offers a step-by-step guide in the build-up of hybrid metaheuristics and to enhance comprehension. In addition, the book contains a range of real-life case studies and their applications. This important text: Includes performance analysis of the hybrid metaheuristics as related to their conventional counterparts Offers an in-depth analysis of a range of optimization algorithms Highlights a review of data clustering Contains a detailed overview of different standard metaheuristics in current use Presents a step-by-step guide to the build-up of hybrid metaheuristics Offers real-life case studies and applications Written for researchers, students and academics in computer science, mathematics, and engineering, Recent Advances in Hybrid Metaheuristics for Data Clustering provides a text that explores the current data clustering approaches using a range of computational intelligence techniques.

Data Mining and Knowledge Discovery with Evolutionary Algorithms

Author : Alex A. Freitas
Publisher : Springer Science & Business Media
ISBN 13 : 3662049236
Total Pages : 272 pages
Book Rating : 4.6/5 (62 download)

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Book Synopsis Data Mining and Knowledge Discovery with Evolutionary Algorithms by : Alex A. Freitas

Download or read book Data Mining and Knowledge Discovery with Evolutionary Algorithms written by Alex A. Freitas and published by Springer Science & Business Media. This book was released on 2013-11-11 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book integrates two areas of computer science, namely data mining and evolutionary algorithms. Both these areas have become increasingly popular in the last few years, and their integration is currently an active research area. In general, data mining consists of extracting knowledge from data. The motivation for applying evolutionary algorithms to data mining is that evolutionary algorithms are robust search methods which perform a global search in the space of candidate solutions. This book emphasizes the importance of discovering comprehensible, interesting knowledge, which is potentially useful for intelligent decision making. The text explains both basic concepts and advanced topics

Metaheuristic Clustering

Author : Swagatam Das
Publisher : Springer
ISBN 13 : 3540939644
Total Pages : 266 pages
Book Rating : 4.5/5 (49 download)

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Book Synopsis Metaheuristic Clustering by : Swagatam Das

Download or read book Metaheuristic Clustering written by Swagatam Das and published by Springer. This book was released on 2009-01-30 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt: Cluster analysis means the organization of an unlabeled collection of objects or patterns into separate groups based on their similarity. The task of computerized data clustering has been approached from diverse domains of knowledge like graph theory, multivariate analysis, neural networks, fuzzy set theory, and so on. Clustering is often described as an unsupervised learning method but most of the traditional algorithms require a prior specification of the number of clusters in the data for guiding the partitioning process, thus making it not completely unsupervised. Modern data mining tools that predict future trends and behaviors for allowing businesses to make proactive and knowledge-driven decisions, demand fast and fully automatic clustering of very large datasets with minimal or no user intervention. In this volume, we formulate clustering as an optimization problem, where the best partitioning of a given dataset is achieved by minimizing/maximizing one (single-objective clustering) or more (multi-objective clustering) objective functions. Using several real world applications, we illustrate the performance of several metaheuristics, particularly the Differential Evolution algorithm when applied to both single and multi-objective clustering problems, where the number of clusters is not known beforehand and must be determined on the run. This volume comprises of 7 chapters including an introductory chapter giving the fundamental definitions and the last Chapter provides some important research challenges. Academics, scientists as well as engineers engaged in research, development and application of optimization techniques and data mining will find the comprehensive coverage of this book invaluable.

Applications of Advanced Optimization Techniques in Industrial Engineering

Author : Abhinav Goel
Publisher : CRC Press
ISBN 13 : 1000544869
Total Pages : 243 pages
Book Rating : 4.0/5 (5 download)

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Book Synopsis Applications of Advanced Optimization Techniques in Industrial Engineering by : Abhinav Goel

Download or read book Applications of Advanced Optimization Techniques in Industrial Engineering written by Abhinav Goel and published by CRC Press. This book was released on 2022-03-09 with total page 243 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides different approaches used to analyze, draw attention, and provide an understanding of the advancements in the optimization field across the globe. It brings all of the latest methodologies, tools, and techniques related to optimization and industrial engineering into a single volume to build insights towards the latest advancements in various domains. Applications of Advanced Optimization Techniques in Industrial Engineering includes the basic concept of optimization, techniques, and applications related to industrial engineering. Concepts are introduced in a sequential way along with explanations, illustrations, and solved examples. The book goes on to explore applications of operations research and covers empirical properties of a variety of engineering disciplines. It presents network scheduling, production planning, industrial and manufacturing system issues, and their implications in the real world. The book caters to academicians, researchers, professionals in inventory analytics, business analytics, investment managers, finance firms, storage-related managers, and engineers working in engineering industries and data management fields.

Intelligent Data Engineering and Automated Learning - IDEAL 2004

Author : Zhen Rong Yang
Publisher : Springer Science & Business Media
ISBN 13 : 3540228810
Total Pages : 868 pages
Book Rating : 4.5/5 (42 download)

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Book Synopsis Intelligent Data Engineering and Automated Learning - IDEAL 2004 by : Zhen Rong Yang

Download or read book Intelligent Data Engineering and Automated Learning - IDEAL 2004 written by Zhen Rong Yang and published by Springer Science & Business Media. This book was released on 2004-08-13 with total page 868 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 5th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2004, held in Exeter, UK, in August 2004. The 124 revised full papers presented were carefully reviewed and selected from 272 submissions. The papers are organized in topical sections on bioinformatics, data mining and knowledge engineering, learning algorithms and systems, financial engineering, and agent technologies.

Data Clustering and Image Segmentation Through Genetic Algorithms

Author : Sujata Dash
Publisher : Engineering Science Reference
ISBN 13 : 9781522563198
Total Pages : 160 pages
Book Rating : 4.5/5 (631 download)

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Book Synopsis Data Clustering and Image Segmentation Through Genetic Algorithms by : Sujata Dash

Download or read book Data Clustering and Image Segmentation Through Genetic Algorithms written by Sujata Dash and published by Engineering Science Reference. This book was released on 2018-08-03 with total page 160 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book provides a broad overview of genetic algorithms, clustering algorithms influenced by genetic algorithms, improvements attained in the field of image segmentation and their application by using genetic algorithms. It also explores the comparative analysis of earlier methods and the recent ones proposed with the use of genetic algorithms"--

Data-Driven Evolutionary Optimization

Author : Yaochu Jin
Publisher : Springer Nature
ISBN 13 : 3030746402
Total Pages : 393 pages
Book Rating : 4.0/5 (37 download)

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Book Synopsis Data-Driven Evolutionary Optimization by : Yaochu Jin

Download or read book Data-Driven Evolutionary Optimization written by Yaochu Jin and published by Springer Nature. This book was released on 2021-06-28 with total page 393 pages. Available in PDF, EPUB and Kindle. Book excerpt: Intended for researchers and practitioners alike, this book covers carefully selected yet broad topics in optimization, machine learning, and metaheuristics. Written by world-leading academic researchers who are extremely experienced in industrial applications, this self-contained book is the first of its kind that provides comprehensive background knowledge, particularly practical guidelines, and state-of-the-art techniques. New algorithms are carefully explained, further elaborated with pseudocode or flowcharts, and full working source code is made freely available. This is followed by a presentation of a variety of data-driven single- and multi-objective optimization algorithms that seamlessly integrate modern machine learning such as deep learning and transfer learning with evolutionary and swarm optimization algorithms. Applications of data-driven optimization ranging from aerodynamic design, optimization of industrial processes, to deep neural architecture search are included.

Evolutionary Algorithms in Engineering Applications

Author : Dipankar Dasgupta
Publisher : Springer Science & Business Media
ISBN 13 : 9783540620211
Total Pages : 584 pages
Book Rating : 4.6/5 (22 download)

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Book Synopsis Evolutionary Algorithms in Engineering Applications by : Dipankar Dasgupta

Download or read book Evolutionary Algorithms in Engineering Applications written by Dipankar Dasgupta and published by Springer Science & Business Media. This book was released on 1997-05-20 with total page 584 pages. Available in PDF, EPUB and Kindle. Book excerpt: Evolutionary algorithms - an overview. Robust encodings in genetic algorithms. Genetic engineering and design problems. The generation of form using an evolutionary approach. Evolutionary optimization of composite structures. Flaw detection and configuration with genetic algorithms. A genetic algorithm approach for river management. Hazards in genetic design methodologies. The identification and characterization of workload classes. Lossless and Lossy data compression. Database design with genetic algorithms. Designing multiprocessor scheduling algorithms using a distributed genetic algorithm system. Prototype based supervised concept learning using genetic algorithms. Prototyping intelligent vehicle modules using evolutionary algorithms. Gate-level evolvable hardware: empirical study and application. Physical design of VLSI circuits and the application of genetic algorithms. Statistical generalization of performance-related heuristcs for knowledge-lean applications. Optimal scheduling of thermal power generation using evolutionary algorithms. Genetic algorithms and genetic programming for control. Global structure evolution and local parameter learning for control system model reductions. Adaptive recursive filtering using evolutionary algorithms. Numerical techniques for efficient sonar bearing and range searching in the near field using genetic algorithms. Signal design for radar imaging in radar astronomy: genetic optimization. Evolutionary algorithms in target acquisition and sensor fusion. Strategies for the integration of evolutionary/ adaptive search with the engineering design process. identification of mechanical inclusions. GeneAS: a robust optimal design technique for mechanical component design. Genetic algorithms for optimal cutting. Practical issues and recent advances in Job- and Open-Shop scheduling. The key steps to achieve mass customization.